



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer




@dataclass
class    snake_case__		(			__lowerCAmelCase	):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE          =      42




class    snake_case__		(			__lowerCAmelCase							,	__lowerCAmelCase	):






     """simple docstring"""


     @register_to_config
     def __init__(			self    :    List[Any],					_snake_case    :    List[Any] = 3,					_snake_case    :    Union[str, Any] = 3,					_snake_case    :    Optional[int] = ("DownEncoderBlock2D",),					_snake_case    :    Tuple = ("UpDecoderBlock2D",),					_snake_case    :    Optional[Any] = (6_4,),					_snake_case    :    int = 1,					_snake_case    :    str = "silu",					_snake_case    :    Optional[int] = 3,					_snake_case    :    Optional[int] = 3_2,					_snake_case    :    List[str] = 2_5_6,					_snake_case    :    Any = 3_2,					_snake_case    :    Tuple = None,					_snake_case    :    Any = 0.1_8_2_1_5,					_snake_case    :    List[Any] = "group",					)  ->Optional[Any]:
            super().__init__()

            # pass init params to Encoder
            snake_case__				:      Optional[Any]        =      Encoder(
                in_channels=_lowerCamelCase,					out_channels=_lowerCamelCase,					down_block_types=_lowerCamelCase,					block_out_channels=_lowerCamelCase,					layers_per_block=_lowerCamelCase,					act_fn=_lowerCamelCase,					norm_num_groups=_lowerCamelCase,					double_z=_lowerCamelCase,					)

            snake_case__				:      Tuple        =      vq_embed_dim if vq_embed_dim is not None else latent_channels

            snake_case__				:      List[str]        =      nn.Convad(_lowerCamelCase,					_lowerCamelCase,					1   )
            snake_case__				:      int        =      VectorQuantizer(_lowerCamelCase,					_lowerCamelCase,					beta=0.2_5,					remap=_lowerCamelCase,					sane_index_shape=_lowerCamelCase   )
            snake_case__				:      Union[str, Any]        =      nn.Convad(_lowerCamelCase,					_lowerCamelCase,					1   )

            # pass init params to Decoder
            snake_case__				:      Optional[int]        =      Decoder(
                in_channels=_lowerCamelCase,					out_channels=_lowerCamelCase,					up_block_types=_lowerCamelCase,					block_out_channels=_lowerCamelCase,					layers_per_block=_lowerCamelCase,					act_fn=_lowerCamelCase,					norm_num_groups=_lowerCamelCase,					norm_type=_lowerCamelCase,					)






     @apply_forward_hook
     def   lowercase_  (			self    :    List[Any],					_snake_case    :    Dict,					_snake_case    :    List[str] = True   )  ->Dict:
            snake_case__				:      Optional[int]        =      self.encoder(_lowerCamelCase   )
            snake_case__				:      Tuple        =      self.quant_conv(_lowerCamelCase   )

            if not return_dict:
                   return (h,)

            return VQEncoderOutput(latents=_lowerCamelCase   )






     @apply_forward_hook
     def   lowercase_  (			self    :    List[Any],					_snake_case    :    List[Any],					_snake_case    :    List[Any] = False,					_snake_case    :    int = True   )  ->Any:
            # also go through quantization layer
            if not force_not_quantize:
                   snake_case__      ,							snake_case__      ,							snake_case__				:      Union[str, Any]        =      self.quantize(_lowerCamelCase   )
            else:
                   snake_case__				:      Tuple        =      h
            snake_case__				:      int        =      self.post_quant_conv(_lowerCamelCase   )
            snake_case__				:      str        =      self.decoder(_lowerCamelCase,					quant if self.config.norm_type == 'spatial' else None   )

            if not return_dict:
                   return (dec,)

            return DecoderOutput(sample=_lowerCamelCase   )






     def   lowercase_  (			self    :    Union[str, Any],					_snake_case    :    str,					_snake_case    :    List[Any] = True   )  ->Union[str, Any]:
            snake_case__				:      Dict        =      sample
            snake_case__				:      Any        =      self.encode(_lowerCamelCase   ).latents
            snake_case__				:      Union[str, Any]        =      self.decode(_lowerCamelCase   ).sample

            if not return_dict:
                   return (dec,)

            return DecoderOutput(sample=_lowerCamelCase   )







"""simple docstring"""


from __future__ import annotations

a				='#'






class   __UpperCAmelCase		:

				def __init__( self ):
										lowerCamelCase__   ={}

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =self._trie
										for char in text:
																if char not in trie:
																						lowerCamelCase__   ={}
																lowerCamelCase__   =trie[char]
										lowerCamelCase__   =True

				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =self._trie
										for char in prefix:
																if char in trie:
																						lowerCamelCase__   =trie[char]
																else:
																						return []
										return self._elements(_lowerCamelCase )






				def 						_a      ( self      ,					_lowerCamelCase ):
										lowerCamelCase__   =[]
										for c, v in d.items():
																lowerCamelCase__   =[" "] if c == END else [(c + s) for s in self._elements(_lowerCamelCase )]
																result.extend(_lowerCamelCase )
										return tuple(_lowerCamelCase )





a				=Trie()
a				=('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
	trie.insert_word(word)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple:




						'''simple docstring'''



						lowerCamelCase__   =trie.find_word(__lowerCAmelCase							)
						return tuple(string + word for word in suffixes							)





def      lowerCamelCase_					(   )				->   None:




						'''simple docstring'''



						print(autocomplete_using_trie("de"							)							)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()


